CN108572378A - An Adaptive Filtering Algorithm for Signal Preprocessing in Satellite Navigation System - Google Patents

Abstract

The invention discloses an adaptive α-β-γ filtering algorithm for signal preprocessing in a satellite navigation system. In view of the low accuracy of observation pseudorange and Doppler in traditional satellite navigation system, especially when the target object has high dynamics, the received signal is noisy and easy to diverge, etc., an adaptive α‑β is proposed. ‑γ filtering algorithm. In order to solve the problems existing in the existing technology, the algorithm is based on the fixed parameter α‑β‑γ filter, and adjusts the filter coefficient according to the receiver’s dynamic self-adaption. Le pre-filtering. The self-adaptive α-β-γ filtering algorithm for signal preprocessing in the satellite navigation system proposed by the present invention is applicable to single satellite navigation systems, multi-constellation navigation systems and combined navigation systems, improves the accuracy of receiver positioning and fixed speed, and can be used in complex It satisfies convergence and filtering stability in motion scenes, and has strong theoretical value and engineering application value of satellite navigation receiver design.

Description

An Adaptive Filtering Algorithm for Signal Preprocessing in Satellite Navigation System

technical field

The invention belongs to the field of satellite positioning and navigation, and specifically relates to an adaptive α-β-γ filtering algorithm for signal preprocessing in a satellite navigation system.

Background technique

Global Navigation Satellite System (GNSS) has a very wide range of applications: navigation and positioning at all levels of sea, land and air, including ocean navigation and port guidance for ships, car navigation and aircraft route guidance, approach and landing Wait. The current systems mainly include GPS in the United States, BDS in China, GALILEO in Europe, and GLONASS in Russia. The combination of multiple systems has become a trend.

The pre-filtering of the signal by the receiver can greatly improve the accuracy of positioning and speed determination, and reduce the impact of noise. Kalman filter is a relatively mature filter, but it has the disadvantage of excessive calculation, which is difficult for real-time signal processing; α-β filter is commonly used in engineering, the calculation is simple but the filtering accuracy is average, especially in the receiver dynamic The effect is poor in larger scenes; α-β-γ filtering is applied in some scenarios, but the α-β-γ filter with fixed parameters is difficult to adapt to the situation of target maneuvering changes, and in practice it often cannot satisfy both convergence speed and Filtering accuracy requirements must be properly compromised.

In the traditional satellite navigation system, the accuracy of observation pseudorange and Doppler is low, especially when the target object has high dynamics, it is difficult to meet the requirements of convergence and filtering accuracy at the same time. To solve the above problems, the present invention proposes an adaptive α-β-γ filtering algorithm.

Contents of the invention

(1) Technical problems to be solved

In order to solve the problems existing in the prior art, the present invention proposes an adaptive α-β-γ filter algorithm for signal preprocessing in a satellite navigation system. It is suitable for pseudo-range and Doppler pre-filtering processing in static, uniform-speed, and high-dynamic scenes, which improves the results of receiver positioning and constant speed, and can meet convergence and filtering stability in complex motion scenes. It has strong satellite Theoretical value and engineering application value of navigation receiver design.

(2) Technical solution

The present invention proposes an adaptive filtering α-β-γ filtering algorithm for signal preprocessing in a satellite navigation system, and the method comprises the following steps:

Step 1: The integrated navigation receiver performs down-conversion, acquisition and tracking, and data synchronization processing on the GNSS signal, and extracts the observed pseudorange and Doppler;

Step 2: The measurement error variance σ _m is initialized, and the prediction error variance σ _p coefficient λ is initialized;

Step 3: Assuming that the acceleration of the receiver is constant for a short period of time, calculate its prediction equation:

in is the filter’s estimate of the receiver’s true position x _k-1 , is the true velocity of the filter to the receiver the estimated value of is the true acceleration of the filter to the receiver The estimated value of , T _s represents the time interval between two adjacent measurement moments.

Step 4: Dynamically update the α-β-γ coefficients according to the motion state:

Step 5: Get the position measurement at the kth epoch Afterwards, the adaptive α-β-γ filter updates the estimated values of receiver position, velocity, and acceleration:

Preferably, the GNSS described in step 1 adopts BDS.

Optionally, the GNSS in step 1 may be GPS, BDS, GALILEO or any combination of the three.

Optionally, if the GNSS described in step 1 is combined with GLONASS and GPS/BDS/GALILEO, since GPS adopts the WGS-84 coordinate system, GALILEO adopts the GTRF coordinate system, and BDS adopts the CGCS2000 coordinate system, the origin of the above three coordinate systems and The coordinate axes are basically the same, and the error between the coordinate systems is very small and can be ignored under non-precision positioning; while GLONASS uses the PZ-90 coordinate system, which needs coordinate conversion with GPS/BDS/GALILEO.

Preferably, in step 2, the initialization value of λ is between 0.05 and 0.20, σ _m is initialized according to system conditions, and σ _p is initialized to 0.

Preferably, the set value of T _s in step 3 is between 1 ms and 3 ms.

Preferably, in step 4 set as Among them, n is 2~3, and λ changes dynamically with the change of acceleration, namely

Preferably, in step 4 That is, β and γ are dynamically adjusted along with α.

Optional, in step 4 Satisfy α>0, 4-2α>β>0.

Preferably, step 5 estimates the position, velocity and acceleration according to the updated parameters.

(3) Beneficial effects

The self-adaptive filtering alpha-beta-gamma filtering algorithm for signal preprocessing in the satellite navigation system proposed by the invention can produce positive and beneficial effects. In the case of large noise and jitter in the received signal, the invention improves the receiver positioning and speed determination results through the adaptive α-β-γ filtering algorithm, and satisfies the convergence and filtering stability in different complex motion scenes , has strong satellite navigation receiver design theoretical value and engineering application value.

Description of drawings

Fig. 1 has shown the self-adaptive α-β-γ filter algorithm flowchart of signal preprocessing in the preferred embodiment of the present invention satellite navigation system;

Fig. 2 has shown adaptive alpha-beta-gamma filtering algorithm in the preferred embodiment of the present invention to static scene motion locus estimation effect figure;

Fig. 3 has shown the adaptive α-β-γ filtering algorithm in the preferred embodiment of the present invention to the estimation effect figure of uniform velocity scene motion track;

Fig. 4 has shown the adaptive α-β-γ filtering algorithm in the preferred embodiment of the present invention to the scene motion trajectory estimation effect diagram of uniform acceleration;

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

Fig. 1 has shown the self-adaptive α-β-γ filter algorithm flowchart of signal preprocessing in the preferred embodiment of the present invention satellite navigation system;

As shown in Figure 1, the method flowchart of the preferred embodiment of the present invention mainly includes the following steps:

Step 1: The integrated navigation receiver performs down-conversion, acquisition and tracking, and data synchronization processing on the GNSS signal, and extracts the observed pseudorange and Doppler;

Step 2: The measurement error variance σ _m is initialized, and the prediction error variance σ _p coefficient λ is initialized;

Step 3: Assuming that the acceleration of the receiver remains unchanged for a short period of time, calculate its prediction equation;

Step 4: Dynamically update the α-β-γ coefficient according to the motion state;

Step 5: Get the position measurement at the kth epoch After that, the adaptive α-β-γ filter updates the estimated values of receiver position, velocity and acceleration;

In a specific embodiment of the present invention, the GNSS described in step 1 adopts BDS. In addition, the GNSS described in step 1 may also use GPS. Optionally, the GNSS in step 1 may be GPS or BDS or GALILEO or any combination of the above three. Optionally, if the GNSS mentioned in step 1 is combined with GLONASS and GPS/BDS/GALILEO, since GPS adopts the WGS-84 coordinate system, GALILEO adopts the GTRF coordinate system, and BDS adopts the CGCS2000 coordinate system, the origin of the above three coordinate systems and The coordinate axes are basically the same, and the error between the coordinate systems is very small and can be ignored under non-precision positioning; while GLONASS uses the PZ-90 coordinate system and needs to perform coordinate conversion with GPS/BDS/GALILEO.

In step 2, the initialization value of λ is between 0.05 and 0.20, σ _m is initialized according to the system conditions, and σ _p is initialized to 0.

In step 3, the T _s setting value is between 1ms and 3ms.

step 4 set as Among them, n is 2~3, and λ changes dynamically with the change of acceleration, namely

step 4 That is, β and γ are dynamically adjusted along with α. optional step 4 Satisfy α>0, 4-2α>β>0.

In step 5, the position, velocity and acceleration are estimated according to the updated parameters.

Fig. 2 has shown adaptive alpha-beta-gamma filtering algorithm in the preferred embodiment of the present invention to static scene motion locus estimation effect diagram;

In a specific embodiment of the present invention, noise with a variance of 10 is added to the static observation signal, and the estimated curve obtained after adaptive filtering is basically consistent with the real curve. The variance between the observed value and the real value within 100s of statistics is 0.0897, and the filtering result is relatively Well, that is, the algorithm is adapted to static scenes.

Fig. 3 has shown the adaptive α-β-γ filtering algorithm in the preferred embodiment of the present invention to the estimation effect figure of uniform velocity scene motion track;

In the specific embodiment of the present invention, the noise with variance of 10 is added on the observation signal of uniform velocity motion of 50m/s, and the estimated curve obtained after utilizing the adaptive filtering is basically consistent with the real curve, and the variance of the observed value and the real value within 100s is 0.2168, the filtering result is better, that is, the algorithm is suitable for uniform motion scenes.

Fig. 4 has shown the adaptive α-β-γ filtering algorithm in the preferred embodiment of the present invention to the scene motion trajectory estimation effect diagram of uniform acceleration;

In a specific embodiment of the present invention, noise with a variance of 10 is added to the uniformly accelerated motion observation signal of 20m/s2, and the estimated curve obtained after adaptive filtering is basically consistent with the real curve, and the variance between the observed value and the real value within 100s is counted is 0.3085, the filtering result is better, that is, the algorithm is suitable for uniform acceleration motion scenes.

In summary, the present invention proposes an adaptive α-β-γ filter algorithm for signal preprocessing in a satellite navigation system. This method aims at the low accuracy of observation pseudorange and Doppler in the traditional satellite navigation system, especially when the dynamics of the target object is high, the received signal is noisy and easy to diverge, etc., and proposes an adaptive α-β - gamma filtering algorithm. Based on the fixed parameter α-β-γ filter, the algorithm adjusts the filter coefficient according to the receiver's dynamic self-adaption, which is suitable for pseudo-range and Doppler pre-filtering in static, uniform speed, and high-dynamic scenes, improving positioning, Speed accuracy. The self-adaptive α-β-γ filter algorithm for signal preprocessing in the satellite navigation system proposed by the present invention is suitable for single satellite navigation systems, multi-constellation navigation systems and combined navigation systems, improves the accuracy of receiver positioning and fixed speed, and can be used in complex It satisfies convergence and filtering stability in motion scenes, and has strong theoretical value and engineering application value of satellite navigation receiver design.

It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and not to limit the present invention. Therefore, any modifications made without departing from the spirit and scope of the present invention shall be included in the protection scope of the present invention. The appended claims of the present invention are intended to cover all changes and modifications that come within the scope and metespan of the appended claims, or equivalents of such scope and metesight.

Claims (8)

1. the adaptive alpha-beta-γ filtering algorithms of Signal Pretreatment in a kind of satellite navigation system, which is characterized in that the method Include the following steps：

Step 1：GNSS signal is carried out down coversion, acquisition and tracking, data synchronization processing by combined navigation receiver, extracts observed quantity Pseudorange and Doppler；

Step 2：Measurement error variances sigma_mInitialization, prediction error variance σ_pCoefficient lambda initializes；

Step 3：It is assumed that the acceleration of receiver is constant within a bit of time, its predictive equation is calculated；

Step 4：According to motion state dynamic update alpha-beta-gamma coefficient；

Step 5：Obtaining the position measurements of kth epochAfterwards, adaptive alpha-beta-γ filter updates receiver location, speed The estimated value of degree, acceleration.

2. the adaptive alpha-beta-γ filtering algorithms of Signal Pretreatment in a kind of satellite navigation system according to claim 1, It is characterized in that：GNSS described in step 1 can be arbitrary combinations of the GPS either between BDS or GALILEO or three.

3. the adaptive alpha-beta-γ filtering of Signal Pretreatment is calculated in a kind of satellite navigation system according to claim 1 or 2 Method, it is characterised in that：If the GNSS is combined using GLONASS and GPS/BDS/GALILEO, since GPS is used WGS-84 coordinate systems, GALILEO use GTRF coordinate systems, BDS use CGCS2000 coordinate systems, above three's coordinate origin and Reference axis is almost the same, and error is very small between coordinate system and can ignore under non-precision positioning；And GLONASS uses PZ- 90 coordinate systems need to carry out coordinate conversion with GPS/BDS/GALILEO.

4. the adaptive alpha-beta-γ filtering algorithms of Signal Pretreatment in a kind of satellite navigation system according to claim 1, It is characterized in that：Between λ initialization values are 0.05~0.20 in step 2, σ_mIt is initialized according to system situation, σ_pInitialization It is 0.

5. the adaptive alpha-beta-γ filtering algorithms of Signal Pretreatment in a kind of satellite navigation system according to claim 1, It is characterized in that：T in step 3_sSetting value is between 1ms~3ms.

6. the adaptive alpha-beta-γ filtering algorithms of Signal Pretreatment in a kind of satellite navigation system according to claim 1, It is characterized in that：In step 4It is set asWherein n is 2~3, and λ is with acceleration The variation of degree and dynamic changes, i.e.,

7. the adaptive alpha-beta-γ filtering algorithms of Signal Pretreatment in a kind of satellite navigation system according to claim 1, It is characterized in that：β, γ are with α dynamic adjustment in step 4, i.e.,： In optional step 4 Meet α>0,4-2 α>β>0.

8. the adaptive alpha-beta-γ filtering algorithms of Signal Pretreatment in a kind of satellite navigation system according to claim 1, It is characterized in that：Position, speed, acceleration are estimated according to updated parameter in step 5.